Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification

Correlating transcription and protein expression profiles of immune biomarkers following lipopolysaccharide exposure in lung epithelial cells

Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa. Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Chorioamnionitis-exposure alters serum cytokine trends in premature neonates

OBJECTIVES

Determine if chronologic age and/or chorioamnionitis exposure alter normal serum cytokine and chemokine levels in uninfected preterm neonates during their initial NICU stay.

STUDY DESIGN

A 7-plex immunoassay measured levels of serum IL-1β, IL-6, IL-8, IL-10, TNF-α, CCL2, and CCL3 longitudinally from chorioamnionitis-exposed and unexposed preterm neonates under 33 weeks' gestation.

RESULTS

Chorioamnionitis-exposed and unexposed preterm neonates demonstrated differences in the trends of IL-1β, IL-6, IL-8, IL-10, TNF-α, and CCL2 over the first month of life. The unexposed neonates demonstrated elevated levels of these inflammatory markers in the first two weeks of life with a decrease by the third week of life, while the chorioamnionitis-exposed neonates demonstrated differences over time without a predictable pattern. Chorioamnionitis-exposed and unexposed neonates demonstrated altered IL-10 and TNF-α trajectories over the first twelve weeks of life.

CONCLUSION

Chorioamnionitis induces a state of immune dysregulation in preterm neonates that persists beyond the immediate neonatal period.

Detection of biomarkers for filoviral infection with a silicon photonic resonator platform

This protocol describes the use of silicon photonic microring resonator sensors for detection of Ebola virus (EBOV) and Sudan virus (SUDV) soluble glycoprotein (sGP). This protocol encompasses biosensor functionalization of silicon microring resonator chips, detection of protein biomarkers in sera, preparing calibration standards for analytical validation, and quantification of the results from these experiments. This protocol is readily adaptable toward other analytes, including cytokines, chemokines, nucleic acids, and viruses. For complete details on the use and execution of this protocol, please refer to Qavi et al. (2022).

Rapid detection of an Ebola biomarker with optical microring resonators

Ebola virus (EBOV) is a highly infectious pathogen, with a case mortality rate as high as 89%. Rapid therapeutic treatments and supportive measures can drastically improve patient outcome; however, the symptoms of EBOV disease (EVD) lack specificity from other endemic diseases. Given the high mortality and significant symptom overlap, there is a critical need for sensitive, rapid diagnostics for EVD. Facile diagnosis of EVD remains a challenge. Here, we describe a rapid and sensitive diagnostic for EVD through microring resonator sensors in conjunction with a unique biomarker of EBOV infection, soluble glycoprotein (sGP). Microring resonator sensors detected sGP in under 40 min with a limit of detection (LOD) as low as 1.00 ng/mL in serum. Furthermore, we validated our assay with the detection of sGP in serum from EBOV-infected non-human primates. Our results demonstrate the utility of a high-sensitivity diagnostic platform for detection of sGP for diagnosis of EVD.

Risk assessment of latent tuberculosis infection through a multiplexed cytokine biosensor assay and machine learning feature selection

Accurate detection and risk stratification of latent tuberculosis infection (LTBI) remains a major clinical and public health problem. We hypothesize that multiparameter strategies that probe immune responses to Mycobacterium tuberculosis can provide new diagnostic insights into not only the status of LTBI infection, but also the risk of reactivation. After the initial proof-of-concept study, we developed a 13-plex immunoassay panel to profile cytokine release from peripheral blood mononuclear cells stimulated separately with Mtb-relevant and non-specific antigens to identify putative biomarker signatures. We sequentially enrolled 65 subjects with various risk of TB exposure, including 32 subjects with diagnosis of LTBI. Random Forest feature selection and statistical data reduction methods were applied to determine cytokine levels across different normalized stimulation conditions. Receiver Operator Characteristic (ROC) analysis for full and reduced feature sets revealed differences in biomarkers signatures for LTBI status and reactivation risk designations. The reduced set for increased risk included IP-10, IL-2, IFN-γ, TNF-α, IL-15, IL-17, CCL3, and CCL8 under varying normalized stimulation conditions. ROC curves determined predictive accuracies of > 80% for both LTBI diagnosis and increased risk designations. Our study findings suggest that a multiparameter diagnostic approach to detect normalized cytokine biomarker signatures might improve risk stratification in LTBI.

The Potential Use of Peripheral Blood Mononuclear Cells as Biomarkers for Treatment Response and Outcome Prediction in Psychiatry: A Systematic Review

BACKGROUND

Psychiatric disorders have a major impact on the global burden of disease while therapeutic interventions remain insufficient to adequately treat a large number of patients. Regrettably, the efficacy of several psychopharmacological treatment regimens becomes apparent only after 4-6 weeks, and at this point, a significant number of patients present as non-responsive. As such, many patients go weeks/months without appropriate treatment or symptom management. Adequate biomarkers for treatment success and outcome prediction are thus urgently needed.

OBJECTIVE

With this systematic review, we provide an overview of the use of peripheral blood mononuclear cells (PBMCs) and their signaling pathways in evaluating and/or predicting the effectiveness of different treatment regimens in the course of psychiatric illnesses. We highlight PBMC characteristics that (i) reflect treatment presence, (ii) allow differentiation of responders from non-responders, and (iii) prove predictive at baseline with regard to treatment outcome for a broad range of psychiatric intervention strategies.

REVIEW METHODS

A PubMed database search was performed to extract papers investigating the relation between any type of PBMC characteristic and treatment presence and/or outcome in patients suffering from severe mental illness. Criteria for eligibility were: written in English; psychiatric diagnosis based on DSM-III-R or newer; PBMC isolation via gradient centrifugation; comparison between treated and untreated patients via PBMC features; sample size ≥ n = 5 per experimental group. Papers not researching in vivo treatment effects between patients and healthy controls, non-clinical trials, and non-hypothesis-/data-driven (e.g., -omics designs) approaches were excluded.

DATA SYNTHESIS

Twenty-nine original articles were included and qualitatively summarized. Antidepressant and antipsychotic treatments were mostly reflected by intracellular inflammatory markers while intervention with mood stabilizers was evidenced through cell maturation pathways. Lastly, cell viability parameters mirrored predominantly non-pharmacological therapeutic strategies. As for response prediction, PBMC (subtype) counts and telomerase activity seemed most promising for antidepressant treatment outcome determination; full length brain-derived neurotrophic factor (BDNF)/truncated BDNF were shown to be most apt to prognosticate antipsychotic treatment.

CONCLUSIONS

We conclude that, although inherent limitations to and heterogeneity in study designs in combination with the scarce number of original studies hamper unambiguous identification, several PBMC characteristics-mostly related to inflammatory pathways and cell viability-indeed show promise towards establishment as clinically relevant treatment biomarkers.

Recent advances in environmental and clinical analysis using microring resonator-based sensors

Progress in the development of biosensors has dramatically improved analytical techniques. Biosensors have advantages over more conventional analytical techniques arising from attributes such as straightforward analyses, higher throughput, miniaturization, smaller sample input, and lower cost. Microring optical resonators have emerged in the area of optical sensors as an exceptional choice due to their sensitivity, ease of fabrication, multiplexity capability and label-free detection. In this paper, the sensing principle of these sensors is described. In addition, we summarize and highlight their most recent and relevant applications in environmental and clinical detection analysis.